Pytdc

If you're doing drug discovery ML, this gives you standardized datasets and benchmarks for the entire therapeutics pipeline. It covers single-instance predictions like ADME and toxicity, multi-instance tasks like drug-target interactions, and generation workflows for novel molecules. The nice part is the scaffold splits and cold-split methods that actually test generalization, plus molecular oracles for property optimization. It's from the Therapeutics Data Commons project and wraps everything in a consistent API so you're not hunting down disparate datasets. Worth using if you're benchmarking models on pharmaceutical tasks or need curated train/test splits that reflect real-world drug discovery challenges rather than random shuffles.

skills.sh installs: 2K
GitHub stars: 56.6K
last updated: Aug 21, 2026

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Install

$ npx -y skills add k-dense-ai/scientific-agent-skills --skill pytdc --agent claude-code

Installs into .claude/skills of the current project.

Files

PyTDC (Therapeutics Data Commons)

Use the official PyTDC distribution (import tdc) to discover therapeutic ML tasks, load approved datasets, apply task-appropriate splits, evaluate predictions, and work with curated benchmark groups. Prefer package metadata over copied dataset lists, and plan network/storage effects before constructing any loader.